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package llm
import (
"context"
"log"
"math"
"slices"
"strings"
"sync"
"time"
"github.com/jonasknobloch/mbpe"
"go.jknobloc.com/x/dataset"
)
func (e *Evaluator) Perplexity(data *dataset.ParquetReader, window, stride int) (float64, error) {
docs := make([]string, 0)
n := 0
for d := range data.Texts("text") {
if n > 5 {
break
}
docs = append(docs, d)
n++
}
tokens := toInt64(e.tokenizer.Tokenize(strings.Join(docs, "\n\n")))[:10240] // TODO performance
batchSize := 1
if len(tokens) < window {
return 0, nil // TODO handle
}
windows := ((len(tokens) - window) / stride) + 1
jobs := (windows + batchSize - 1) / batchSize
pb := mbpe.NewProgressBar("Perplexity", 20, jobs, time.Now())
ctx, cancel := context.WithCancel(context.Background())
defer cancel()
done := make(chan struct{})
go func(ctx context.Context) {
main:
for {
select {
case <-ctx.Done():
break main
default:
time.Sleep(time.Second * 1)
e.mutex.RLock()
j := e.jobs
e.mutex.RUnlock()
pb.Update(j)
pb.Print()
if j >= jobs {
break main
}
}
}
pb.Finish()
close(done)
}(ctx)
if err := e.schedule(tokens, window, stride, batchSize); err != nil {
log.Fatal(err)
}
<-done
totalNLL := float64(0)
totalTokens := 0
for _, r := range e.results {
totalNLL += r.nll
totalTokens += r.n
}
average := totalNLL / float64(totalTokens)
return math.Exp(average), nil
}
func (e *Evaluator) schedule(tokens []int64, contextSize, stride, batchSize int) error {
jobs := make(chan *job)
var wg sync.WaitGroup
devices := make([]int, len(e.models))
for i := range len(devices) {
devices[i] = i
}
devicePool := newPool[int](devices...)
for d := 0; d < devicePool.Len(); d++ {
wg.Add(1)
go func() {
defer wg.Done()
for j := range jobs {
device := devicePool.Acquire()
e.execute(j, device)
devicePool.Release(device)
e.mutex.Lock()
for _, p := range j.results {
e.results = append(e.results, p)
}
e.jobs++
e.mutex.Unlock()
}
}()
}
j := newJob(batchSize)
// 0 to 1023: full logits
// 512 to 1535: 1024 upwards
// 1024 to 2047: 1536 upwards
// ...
seen := 1 // first token as context
n := 0
// for i := 0; i < len(tokens); i += stride {
for i := 0; i+contextSize <= len(tokens); i += stride {
// if (len(j.positions) == batchSize) || i+stride > len(tokens) {
if len(j.positions) == batchSize {
jobs <- j
j = newJob(batchSize)
}
j.positions = append(j.positions, n)
j.tokens = append(j.tokens, tokens[i:min(i+contextSize, len(tokens))]) // TODO verify
j.seen = append(j.seen, seen-i)
seen = i + contextSize
n++
}
if len(j.positions) > 0 {
jobs <- j
}
close(jobs)
wg.Wait()
return nil
}
func (e *Evaluator) execute(j *job, device int) {
if len(j.positions) != 1 {
panic("unimplemented")
}
if j.seen[0] < 1 {
panic("empty context")
}
m := e.models[device]
logits := make([][]float32, 0, len(j.tokens[0]))
// fmt.Println("executing job", j.positions[0])
if _, err := m.Generate(j.tokens[0], 0, &logits); err != nil {
panic(err) // TODO handle
}
nllLogits := logits[j.seen[0]-1 : len(logits)-1]
nnlTargets := toInt(j.tokens[0][j.seen[0]:])
nll := negLogLikelihood(nllLogits, nnlTargets)
j.results = append(j.results, result{
nll: nll,
n: len(nnlTargets),
})
return
}
func negLogLikelihood(logits [][]float32, targets []int) float64 {
if len(logits) != len(targets) {
panic("mismatched input lengths")
}
total := float64(0)
for i, target := range targets {
maxLogit := float64(slices.Max(logits[i]))
sumExp := float64(0)
for _, v := range logits[i] {
sumExp += math.Exp(float64(v) - maxLogit)
}
logSumExp := maxLogit + math.Log(sumExp)
targetLogit := float64(logits[i][target])
logProb := targetLogit - logSumExp
total -= logProb
}
return total
}
|
